BabyTheorist: A Benchmark for Learning to Theorize the World from Observation Alone

We introduce BabyTheorist, a benchmark for controlled training and evaluation of observational theory learning. It comes from a paper posted to arXiv on 9 November 2026. How would we know whether a machine truly understands the world? We argue that one key capability is observational theory learning: acquiring reusable theories from pure observations and applying them to new situations. Current visual reasoning benchmarks focus primarily on the latter, presenting each task as a few-shot puzzle grouped by a common rule. BabyTheorist generates continual streams of before-after visual observations from hidden compositional programs; during training, learners see only the resulting transitions, with no program labels, task identities, or support sets. To support theory learning and evaluation, BabyTheorist controls the program language, a curriculum over program complexity, recurring frequent subprograms, and a continual train-test protocol with level skipping. Seven representative baselines plateau well below ceiling, with the gap widening as the curriculum advances---evidence that BabyTheorist isolates a capability current program-learning approaches do not yet address.

Why it counts

We introduce BabyTheorist, a benchmark for controlled training and evaluation of observational theory learning.

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What is not known

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